Use of satellite remote sensing data in the mapping of global landslide susceptibility

Yang Hong (), Robert Adler and George Huffman

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2007, vol. 43, issue 2, 245-256

Abstract: Satellite remote sensing data has significant potential use in analysis of natural hazards such as landslides. Relying on the recent advances in satellite remote sensing and geographic information system (GIS) techniques, this paper aims to map landslide susceptibility over most of the globe using a GIS-based weighted linear combination method. First, six relevant landslide-controlling factors are derived from geospatial remote sensing data and coded into a GIS system. Next, continuous susceptibility values from low to high are assigned to each of the six factors. Second, a continuous scale of a global landslide susceptibility index is derived using GIS weighted linear combination based on each factor’s relative significance to the process of landslide occurrence (e.g., slope is the most important factor, soil types and soil texture are also primary-level parameters, while elevation, land cover types, and drainage density are secondary in importance). Finally, the continuous index map is further classified into six susceptibility categories. Results show the hot spots of landslide-prone regions include the Pacific Rim, the Himalayas and South Asia, Rocky Mountains, Appalachian Mountains, Alps, and parts of the Middle East and Africa. India, China, Nepal, Japan, the USA, and Peru are shown to have landslide-prone areas. This first-cut global landslide susceptibility map forms a starting point to provide a global view of landslide risks and may be used in conjunction with satellite-based precipitation information to potentially detect areas with significant landslide potential due to heavy rainfall. Copyright Springer Science+Business Media, Inc. 2007

Keywords: Satellite remote sensing; Landslide susceptibility; GIS (search for similar items in EconPapers)
Date: 2007
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Citations: View citations in EconPapers (25)

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DOI: 10.1007/s11069-006-9104-z

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